Drug-drug interactions (DDIs) are crucial for public health and patient safety, which has aroused widespread concern in academia and industry. The existing computational DDI prediction methods are mainly divided into four categories: literature extraction-based, similarity-based, matrix operations-based and network-based. A number of recent studies have revealed that integrating heterogeneous drug features is of significant importance for developing high-accuracy prediction models. Meanwhile, drugs that lack certain features could utilize other features to learn representations. However, it also brings some new challenges such as incomplete data, non-linear relations and heterogeneous properties. In this paper, we propose a multi-modal deep auto-encoders based drug representation learning method named DDI-MDAE, to predict DDIs from large-scale, noisy and sparse data. Our method aims to learn unified drug representations from multiple drug feature networks simultaneously using multi-modal deep auto-encoders. Then, we apply four operators on the learned drug embeddings to represent drug-drug pairs and adopt the random forest classifier to train models for predicting DDIs. The experimental results demonstrate the effectiveness of our proposed method for DDI prediction and significant improvement compared to other state-of-the-art benchmark methods. Moreover, we apply a specialized random forest classifier in the positive-unlabeled (PU) learning setting to enhance the prediction accuracy. Experimental results reveal that the model improved by PU learning outperforms the original method DDI-MDAE by 7.1% and 6.2% improvement in AUPR metric respectively on 3-fold cross-validation (3-CV) and 5-fold cross-validation (5-CV). And in F-measure metric, the improved model gains 10.4% and 8.4% improvement over DDI-MDAE respectively on 3-CV and 5-CV. The usefulness of DDI-MDAE is further demonstrated by case studies.

中文翻译：

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使用基于网络嵌入和正未标记学习的多模态深度自动编码器预测药物相互作用

药物相互作用（DDI）对公共卫生和患者安全至关重要，这引起了学术界和工业界的广泛关注。现有的计算DDI预测方法主要分为四类：基于文献提取、基于相似性、基于矩阵运算和基于网络。最近的一些研究表明，整合异质药物特征对于开发高精度预测模型具有重要意义。同时，缺乏某些特征的药物可以利用其他特征来学习表征。然而，它也带来了一些新的挑战，如数据不完整、非线性关系和异构属性。在本文中，我们提出了一种名为 DDI-MDAE 的基于多模态深度自动编码器的药物表征学习方法，用于大规模预测 DDI，嘈杂和稀疏的数据。我们的方法旨在使用多模态深度自动编码器同​​时从多个药物特征网络中学习统一的药物表示。然后，我们在学习到的药物嵌入上应用四个算子来表示药物-药物对，并采用随机森林分类器来训练预测 DDI 的模型。实验结果证明了我们提出的 DDI 预测方法的有效性和与其他最先进的基准方法相比的显着改进。此外，我们在正未标记 (PU) 学习设置中应用专门的随机森林分类器以提高预测准确性。实验结果表明，通过 PU 学习改进的模型优于原始方法 DDI-MDAE 7.1% 和 6。在 3 倍交叉验证 (3-CV) 和 5 倍交叉验证 (5-CV) 上，AUPR 指标分别提高了 2%。在 F-measure 指标中，改进后的模型在 3-CV 和 5-CV 上分别比 DDI-MDAE 提高了 10.4% 和 8.4%。案例研究进一步证明了 DDI-MDAE 的有用性。